Integrating large language models into search engines could mean a fivefold increase in computing power and huge carbon emissions.
IN EARLY FEBRUARY.
But the excitement over these new tools could be concealing a dirty secret.
Search is the brain computer interface of today, and how we access A.I. as a civilization. Let’s 🥳 democratize it.
Enhancers change rapidly during evolution, but the mechanisms by which new enhancers originate in the genome are mostly unknown. Not all regions of the genome evolve at the same rate and mutations are elevated at late DNA replication time. To understand the role played by mutational processes in enhancer evolution, we leveraged changes in mutation rates across the genome. By examining enhancer turnover in matched healthy and tumor samples in human individuals, we find while enhancers are most common in early replicating regions, new enhancers emerged more often at late replicating regions. Somatic mutations in cancer are consistently elevated in enhancers that have experienced turnover compared to those that are maintained. A similar relationship with DNA replication time is observed in enhancers across mammalian species and in multiple tissue-types. New enhancers appeared almost twice as often in late compared to early replicating regions, independent of transposable elements. We trained a deep learning model to show that new enhancers are enriched for mutations that modify transcription factor (TF) binding. New enhancers are also typically neutrally evolving, enriched in eQTLs, and are more tissue-specific than evolutionarily conserved enhancers. Accordingly, transcription factors that bind to these enhancers, inferred by their binding sequences, are also more recently evolved and more tissue-specific in gene expression. These results demonstrate a relationship between mutation rate, DNA replication time and enhancer evolution across multiple time scales, suggesting these observations are time-invariant principles of genome evolution.
The authors have declared no competing interest.
ChatGPT AI extensions for Google Chrome that generate emails, improve web searches, and write LinkedIn updates.
https://youtube.com/watch?v=fK3AQgt47z4
In 2021, Carbon Robotics unveiled the third-generation of its Autonomous Weeder, a smart farming robot that identifies weeds and then destroys them with high-power lasers. The company now has taken the technology from that robot and built a pull-behind LaserWeeder — and it kills twice as many weeds.
The weedkiller challenge: Weeds compete with plants for space, sunlight, and soil nutrients. They can also make it easier for insect pests to harm crops, so weed control is a top concern for farmers.
Continue reading “Farming robot kills 200,000 weeds per hour with lasers” »
Despite the VC gold rush, this expert is arguing that hype — as opposed to firm data and proven results — is in the generative AI industry driving seat.
Forbes writer Kenrick Cai joins “Forbes Talks” to discuss his landmark report on how generative artificial intelligence will reshape the economy and the world.
Read the full story on Forbes: https://www.forbes.com/sites/kenrickcai/2023/02/02/things-yo…b4aebb5e31
The artificial intelligence-assisted components are lighter and can handle higher structural loads than human-designed components, according to the agency.
“This kind of artificial intelligence that we are currently talking about can sometimes lead to something we call hallucination,” he told the German newspaper.
He added: “This is then expressed in such a way that a machine provides a convincing but completely fictitious answer.”
On Monday Google used a presentation to unveil its AI chatbot called Bard that it hopes will rival ChatGPT.
Enhancers change rapidly during evolution, but the mechanisms by which new enhancers originate in the genome are mostly unknown. Not all regions of the genome evolve at the same rate and mutations are elevated at late DNA replication time. To understand the role played by mutational processes in enhancer evolution, we leveraged changes in mutation rates across the genome. By examining enhancer turnover in matched healthy and tumor samples in human individuals, we find while enhancers are most common in early replicating regions, new enhancers emerged more often at late replicating regions. Somatic mutations in cancer are consistently elevated in enhancers that have experienced turnover compared to those that are maintained. A similar relationship with DNA replication time is observed in enhancers across mammalian species and in multiple tissue-types. New enhancers appeared almost twice as often in late compared to early replicating regions, independent of transposable elements. We trained a deep learning model to show that new enhancers are enriched for mutations that modify transcription factor (TF) binding. New enhancers are also typically neutrally evolving, enriched in eQTLs, and are more tissue-specific than evolutionarily conserved enhancers. Accordingly, transcription factors that bind to these enhancers, inferred by their binding sequences, are also more recently evolved and more tissue-specific in gene expression. These results demonstrate a relationship between mutation rate, DNA replication time and enhancer evolution across multiple time scales, suggesting these observations are time-invariant principles of genome evolution.
